{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import csv\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import re\n",
        "import json\n",
        "import os"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "output": {
          "id": 730003346092053,
          "loadingStatus": "loaded"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['result_tbsize_10000_zch_dlrmv3_kuairand27k', 'result_tbsize_10000_nonzch_dlrmv3_kuairand27k']\n"
          ]
        }
      ],
      "source": [
        "root_folder = \"/home/lizhouyu/home/lizhouyu/zch_results\"\n",
        "stats_folders = [x for x in os.listdir(root_folder) if x.startswith(\"result_tbsize_10000\") and \"kuairand27k\" in x]\n",
        "print(stats_folders)\n",
        "#"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "figure_folder = \"/data/users/lizhouyu/fbsource/fbcode/torchrec/distributed/benchmark/benchmark_zch/figures\"\n",
        "os.makedirs(figure_folder, exist_ok=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "def get_eval_metrics(stats_folder_path):\n",
        "    eval_metrics_file_path = os.path.join(stats_folder_path, \"eval_metrics.csv\")\n",
        "    df_stats = pd.read_csv(eval_metrics_file_path)\n",
        "    # maintain a dictionary of {rank: {metrics: {task_idx: value}}}\n",
        "    rank_eval_metrics_value = {}\n",
        "    for index, row in df_stats.iterrows():\n",
        "        rank_idx = row[\"rank\"]\n",
        "        if rank_idx not in rank_eval_metrics_value:\n",
        "            rank_eval_metrics_value[rank_idx] = {}\n",
        "        # check if the metrics is a list or a single value\n",
        "        for metric_name in [\"ne\", \"auc\", \"mae\", \"mse\"]:\n",
        "            if metric_name not in rank_eval_metrics_value[rank_idx]:\n",
        "                rank_eval_metrics_value[rank_idx][metric_name] = {}\n",
        "            metric_value = row[metric_name]\n",
        "            print(metric_name, metric_value, type(metric_value))\n",
        "            if isinstance(metric_value, str) and (\"[\" in metric_value or \"]\" in metric_value):\n",
        "                metric_value = metric_value.replace(\"[\", \"\").replace(\"]\", \"\").split(\",\")\n",
        "                metric_value = [float(x) for x in metric_value]\n",
        "            if isinstance(metric_value, list):\n",
        "                for task_idx, task_value in enumerate(metric_value):\n",
        "                    rank_eval_metrics_value[rank_idx][metric_name][task_idx] = task_value\n",
        "            else:\n",
        "                rank_eval_metrics_value[rank_idx][metric_name][0] = float(metric_value) if metric_value != \"nan\" else 0.0\n",
        "\n",
        "    # get average qps over all ranks\n",
        "    average_eval_metrics_value = {} # {metrics: {task_idx: average_value_over_all_ranks}}\n",
        "    for rank, metric_name_task_id_metric_value_dict in rank_eval_metrics_value.items():\n",
        "        for metric_name, task_id_metric_value_dict in metric_name_task_id_metric_value_dict.items():\n",
        "            for task_id, metric_value in task_id_metric_value_dict.items():\n",
        "                if metric_name not in average_eval_metrics_value:\n",
        "                    average_eval_metrics_value[metric_name] = {}\n",
        "                if task_id not in average_eval_metrics_value[metric_name]:\n",
        "                    average_eval_metrics_value[metric_name][task_id] = []\n",
        "                average_eval_metrics_value[metric_name][task_id].append(metric_value)\n",
        "    for metric_name, task_id_metric_value_list in average_eval_metrics_value.items():\n",
        "        for task_id, metric_value_list in task_id_metric_value_list.items():\n",
        "            average_eval_metrics_value[metric_name][task_id] = np.mean(metric_value_list)\n",
        "    return average_eval_metrics_value"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [],
      "source": [
        "re_zch_method_name_pattern = re.compile(r\"\\d+_((non)?zch(.*)?)\")\n",
        "re_table_size_pattern = re.compile(r\"result_tbsize_(\\d+)\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "output": {
          "id": 2253043958485501,
          "loadingStatus": "loaded"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "zch_dlrmv3_kuairand27k 10000\n",
            "ne [1.0082555927827888, 0.8971864475100473, 1.081050717655323, 1.0706481519500877, 1.1080358234436172, 1.0940853313027725, 1.0216489750548141, 1.0652245619353071] <class 'str'>\n",
            "auc [0.6327600479125977, 0.7780923247337341, 0.6339844465255737, 0.6193706393241882, 0.5363970398902893, 0.6150850057601929, 0.6271106600761414, 0.6540845036506653] <class 'str'>\n",
            "mae nan <class 'float'>\n",
            "mse nan <class 'float'>\n",
            "ne [1.0082555927827888, 0.8971864475100473, 1.081050717655323, 1.0706481519500877, 1.1080358234436172, 1.0940853313027725, 1.0216489750548141, 1.0652245619353071] <class 'str'>\n",
            "auc [0.6327600479125977, 0.7780923247337341, 0.6339844465255737, 0.6193706393241882, 0.5363970398902893, 0.6150850057601929, 0.6271106600761414, 0.6540845036506653] <class 'str'>\n",
            "mae nan <class 'float'>\n",
            "mse nan <class 'float'>\n",
            "ne [1.0082555927827888, 0.8971864475100473, 1.081050717655323, 1.0706481519500877, 1.1080358234436172, 1.0940853313027725, 1.0216489750548141, 1.0652245619353071] <class 'str'>\n",
            "auc [0.6327600479125977, 0.7780923247337341, 0.6339844465255737, 0.6193706393241882, 0.5363970398902893, 0.6150850057601929, 0.6271106600761414, 0.6540845036506653] <class 'str'>\n",
            "mae nan <class 'float'>\n",
            "mse nan <class 'float'>\n",
            "ne [1.0082555927827888, 0.8971864475100473, 1.081050717655323, 1.0706481519500877, 1.1080358234436172, 1.0940853313027725, 1.0216489750548141, 1.0652245619353071] <class 'str'>\n",
            "auc [0.6327600479125977, 0.7780923247337341, 0.6339844465255737, 0.6193706393241882, 0.5363970398902893, 0.6150850057601929, 0.6271106600761414, 0.6540845036506653] <class 'str'>\n",
            "mae nan <class 'float'>\n",
            "mse nan <class 'float'>\n",
            "nonzch_dlrmv3_kuairand27k 10000\n",
            "ne [1.046645799432464, 0.9173418697036878, 1.0798678758908813, 1.0578123397407198, 1.1531900443020695, 1.1030672323772879, 1.0468359287391686, 1.075794420751179] <class 'str'>\n",
            "auc [0.6255273222923279, 0.7784473299980164, 0.6177278161048889, 0.615269124507904, 0.4703689217567444, 0.593986451625824, 0.6208029389381409, 0.6659070253372192] <class 'str'>\n",
            "mae nan <class 'float'>\n",
            "mse nan <class 'float'>\n",
            "ne [1.046645799432464, 0.9173418697036878, 1.0798678758908813, 1.0578123397407198, 1.1531900443020695, 1.1030672323772879, 1.0468359287391686, 1.075794420751179] <class 'str'>\n",
            "auc [0.6255273222923279, 0.7784473299980164, 0.6177278161048889, 0.615269124507904, 0.4703689217567444, 0.593986451625824, 0.6208029389381409, 0.6659070253372192] <class 'str'>\n",
            "mae nan <class 'float'>\n",
            "mse nan <class 'float'>\n",
            "ne [1.046645799432464, 0.9173418697036878, 1.0798678758908813, 1.0578123397407198, 1.1531900443020695, 1.1030672323772879, 1.0468359287391686, 1.075794420751179] <class 'str'>\n",
            "auc [0.6255273222923279, 0.7784473299980164, 0.6177278161048889, 0.615269124507904, 0.4703689217567444, 0.593986451625824, 0.6208029389381409, 0.6659070253372192] <class 'str'>\n",
            "mae nan <class 'float'>\n",
            "mse nan <class 'float'>\n",
            "ne [1.046645799432464, 0.9173418697036878, 1.0798678758908813, 1.0578123397407198, 1.1531900443020695, 1.1030672323772879, 1.0468359287391686, 1.075794420751179] <class 'str'>\n",
            "auc [0.6255273222923279, 0.7784473299980164, 0.6177278161048889, 0.615269124507904, 0.4703689217567444, 0.593986451625824, 0.6208029389381409, 0.6659070253372192] <class 'str'>\n",
            "mae nan <class 'float'>\n",
            "mse nan <class 'float'>\n"
          ]
        }
      ],
      "source": [
        "metrics_task_id_table_size_zch_method_avetage_value_dict = {} # a dictionary of {metrics: {task_id: {table_size: {zch_method: average_value}}}}\n",
        "for zch_stats_folder in stats_folders:\n",
        "    zch_method_name = re_zch_method_name_pattern.search(zch_stats_folder).group(1)\n",
        "    table_size = int(re_table_size_pattern.search(zch_stats_folder).group(1))\n",
        "    print(zch_method_name, table_size)\n",
        "    zch_stats_file_folder = os.path.join(root_folder, zch_stats_folder)\n",
        "    average_eval_metrics_task_id_value = get_eval_metrics(zch_stats_file_folder)\n",
        "    for metrics_name, task_id_avg_value_dict in average_eval_metrics_task_id_value.items():\n",
        "        for task_id, avg_value in task_id_avg_value_dict.items():\n",
        "            if metrics_name not in metrics_task_id_table_size_zch_method_avetage_value_dict:\n",
        "                metrics_task_id_table_size_zch_method_avetage_value_dict[metrics_name] = {}\n",
        "            if task_id not in metrics_task_id_table_size_zch_method_avetage_value_dict[metrics_name]:\n",
        "                metrics_task_id_table_size_zch_method_avetage_value_dict[metrics_name][task_id] = {}\n",
        "            if table_size not in metrics_task_id_table_size_zch_method_avetage_value_dict[metrics_name][task_id]:\n",
        "                metrics_task_id_table_size_zch_method_avetage_value_dict[metrics_name][task_id][table_size] = {}\n",
        "            metrics_task_id_table_size_zch_method_avetage_value_dict[metrics_name][task_id][table_size][zch_method_name] = avg_value"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "output": {
          "id": 1748400135794163,
          "loadingStatus": "loaded"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{\n",
            "    \"ne\": {\n",
            "        \"0\": {\n",
            "            \"10000\": {\n",
            "                \"zch_dlrmv3_kuairand27k\": 1.0082555927827888,\n",
            "                \"nonzch_dlrmv3_kuairand27k\": 1.046645799432464\n",
            "            }\n",
            "        },\n",
            "        \"1\": {\n",
            "            \"10000\": {\n",
            "                \"zch_dlrmv3_kuairand27k\": 0.8971864475100473,\n",
            "                \"nonzch_dlrmv3_kuairand27k\": 0.9173418697036878\n",
            "            }\n",
            "        },\n",
            "        \"2\": {\n",
            "            \"10000\": {\n",
            "                \"zch_dlrmv3_kuairand27k\": 1.081050717655323,\n",
            "                \"nonzch_dlrmv3_kuairand27k\": 1.0798678758908813\n",
            "            }\n",
            "        },\n",
            "        \"3\": {\n",
            "            \"10000\": {\n",
            "                \"zch_dlrmv3_kuairand27k\": 1.0706481519500877,\n",
            "                \"nonzch_dlrmv3_kuairand27k\": 1.0578123397407198\n",
            "            }\n",
            "        },\n",
            "        \"4\": {\n",
            "            \"10000\": {\n",
            "                \"zch_dlrmv3_kuairand27k\": 1.1080358234436172,\n",
            "                \"nonzch_dlrmv3_kuairand27k\": 1.1531900443020695\n",
            "            }\n",
            "        },\n",
            "        \"5\": {\n",
            "            \"10000\": {\n",
            "                \"zch_dlrmv3_kuairand27k\": 1.0940853313027725,\n",
            "                \"nonzch_dlrmv3_kuairand27k\": 1.1030672323772879\n",
            "            }\n",
            "        },\n",
            "        \"6\": {\n",
            "            \"10000\": {\n",
            "                \"zch_dlrmv3_kuairand27k\": 1.0216489750548141,\n",
            "                \"nonzch_dlrmv3_kuairand27k\": 1.0468359287391686\n",
            "            }\n",
            "        },\n",
            "        \"7\": {\n",
            "            \"10000\": {\n",
            "                \"zch_dlrmv3_kuairand27k\": 1.0652245619353071,\n",
            "                \"nonzch_dlrmv3_kuairand27k\": 1.075794420751179\n",
            "            }\n",
            "        }\n",
            "    },\n",
            "    \"auc\": {\n",
            "        \"0\": {\n",
            "            \"10000\": {\n",
            "                \"zch_dlrmv3_kuairand27k\": 0.6327600479125977,\n",
            "                \"nonzch_dlrmv3_kuairand27k\": 0.6255273222923279\n",
            "            }\n",
            "        },\n",
            "        \"1\": {\n",
            "            \"10000\": {\n",
            "                \"zch_dlrmv3_kuairand27k\": 0.7780923247337341,\n",
            "                \"nonzch_dlrmv3_kuairand27k\": 0.7784473299980164\n",
            "            }\n",
            "        },\n",
            "        \"2\": {\n",
            "            \"10000\": {\n",
            "                \"zch_dlrmv3_kuairand27k\": 0.6339844465255737,\n",
            "                \"nonzch_dlrmv3_kuairand27k\": 0.6177278161048889\n",
            "            }\n",
            "        },\n",
            "        \"3\": {\n",
            "            \"10000\": {\n",
            "                \"zch_dlrmv3_kuairand27k\": 0.6193706393241882,\n",
            "                \"nonzch_dlrmv3_kuairand27k\": 0.615269124507904\n",
            "            }\n",
            "        },\n",
            "        \"4\": {\n",
            "            \"10000\": {\n",
            "                \"zch_dlrmv3_kuairand27k\": 0.5363970398902893,\n",
            "                \"nonzch_dlrmv3_kuairand27k\": 0.4703689217567444\n",
            "            }\n",
            "        },\n",
            "        \"5\": {\n",
            "            \"10000\": {\n",
            "                \"zch_dlrmv3_kuairand27k\": 0.6150850057601929,\n",
            "                \"nonzch_dlrmv3_kuairand27k\": 0.593986451625824\n",
            "            }\n",
            "        },\n",
            "        \"6\": {\n",
            "            \"10000\": {\n",
            "                \"zch_dlrmv3_kuairand27k\": 0.6271106600761414,\n",
            "                \"nonzch_dlrmv3_kuairand27k\": 0.6208029389381409\n",
            "            }\n",
            "        },\n",
            "        \"7\": {\n",
            "            \"10000\": {\n",
            "                \"zch_dlrmv3_kuairand27k\": 0.6540845036506653,\n",
            "                \"nonzch_dlrmv3_kuairand27k\": 0.6659070253372192\n",
            "            }\n",
            "        }\n",
            "    },\n",
            "    \"mae\": {\n",
            "        \"0\": {\n",
            "            \"10000\": {\n",
            "                \"zch_dlrmv3_kuairand27k\": NaN,\n",
            "                \"nonzch_dlrmv3_kuairand27k\": NaN\n",
            "            }\n",
            "        }\n",
            "    },\n",
            "    \"mse\": {\n",
            "        \"0\": {\n",
            "            \"10000\": {\n",
            "                \"zch_dlrmv3_kuairand27k\": NaN,\n",
            "                \"nonzch_dlrmv3_kuairand27k\": NaN\n",
            "            }\n",
            "        }\n",
            "    }\n",
            "}\n"
          ]
        }
      ],
      "source": [
        "print(json.dumps(metrics_task_id_table_size_zch_method_avetage_value_dict, indent=4))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Plot collision rate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "output": {
          "id": 753841307214735,
          "loadingStatus": "loaded"
        }
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# For each metric, plot a bar chart where each bucket is for a task,\n",
        "# and in each bucket, every table_size-zch_method combination has a bar\n",
        "# the Y-axis shows the average metric value of that setting\n",
        "for metric_name in metrics_task_id_table_size_zch_method_avetage_value_dict:\n",
        "    fig = plt.figure(figsize=(8, 6))\n",
        "    font_size = 24\n",
        "    bucket_width = 0.8\n",
        "    x_ticks = []\n",
        "    x_labels = []\n",
        "    for task_id in metrics_task_id_table_size_zch_method_avetage_value_dict[metric_name]:\n",
        "        task_bucket_offest = task_id + 1.0\n",
        "        bucket_bar_label_list = []\n",
        "        bucket_bar_val_list = []\n",
        "        bucket_bar_color_list = []\n",
        "        bucket_bar_shadow_list = []\n",
        "        for table_size in metrics_task_id_table_size_zch_method_avetage_value_dict[metric_name][task_id]:\n",
        "            for zch_method in metrics_task_id_table_size_zch_method_avetage_value_dict[metric_name][task_id][table_size]:\n",
        "                metric_value = metrics_task_id_table_size_zch_method_avetage_value_dict[metric_name][task_id][table_size][zch_method]\n",
        "                bucket_bar_val_list.append(metric_value)\n",
        "                bucket_bar_label_list.append(f\"{table_size}M-{zch_method}\")\n",
        "                if \"nonzch\" in zch_method:\n",
        "                    bucket_bar_color_list.append(\"red\")\n",
        "                    bucket_bar_shadow_list.append((0, 0))\n",
        "                else:\n",
        "                    bucket_bar_color_list.append(\"blue\")\n",
        "                    bucket_bar_shadow_list.append((1, 1))\n",
        "        bucket_x_step = bucket_width / len(bucket_bar_label_list)\n",
        "        bucket_x_offset = bucket_x_step / 2\n",
        "        plt.bar([x * bucket_x_step + task_bucket_offest - bucket_x_offset for x in range(len(bucket_bar_label_list))] , bucket_bar_val_list, width=bucket_x_step, label=bucket_bar_label_list, color=bucket_bar_color_list, alpha=0.5, edgecolor=\"black\", linewidth=2)\n",
        "        x_ticks.append(task_bucket_offest)\n",
        "        x_labels.append(f\"Task\\n{task_id}\")\n",
        "    # maually set legend to show non-zch is a red bar and zch is a blue bar\\\n",
        "    import matplotlib.patches as mpatches\n",
        "    # Create custom legend patches\n",
        "    nonzch_patch = mpatches.Patch(color='red', label='Non-ZCH')\n",
        "    zch_patch = mpatches.Patch(color='blue', label='ZCH')\n",
        "    # Add legend to the figure\n",
        "    plt.legend(handles=[nonzch_patch, zch_patch], loc='upper right')\n",
        "    # plt.legend()\n",
        "    plt.xticks(x_ticks, x_labels, fontsize=font_size)\n",
        "    plt.yticks(fontsize=font_size)\n",
        "    # plt.legend(fontsize=font_size)\n",
        "    plt.title(f\"{metric_name}\", fontsize=font_size)\n",
        "    plt.xlabel(\"Task ID\", fontsize=font_size)\n",
        "    plt.ylabel(\"Average Metric Value\", fontsize=font_size)\n",
        "    plt.grid(True)\n",
        "    plt.tight_layout()\n",
        "    plt.savefig(os.path.join(figure_folder, f\"eval_metrics_{metric_name}.png\"))\n",
        "    plt.show()\n",
        "    plt.close(fig)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# plot the ratio of accumulated_collision_count / accumulated_total_count"
      ]
    }
  ],
  "metadata": {
    "fileHeader": "",
    "fileUid": "48f0276b-0d89-4f9f-9a7a-2038c58a3c2e",
    "isAdHoc": false,
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "bento_kernel_default"
    },
    "language_info": {
      "name": "plaintext"
    },
    "orig_nbformat": 4
  }
}
